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# %pip install geoai-py
# %pip install geoai-py
Import libraries¶
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import geoai
import geoai
Download sample data¶
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raster_url = (
"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif"
)
vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"
raster_url = (
"https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train.tif"
)
vector_url = "https://huggingface.co/datasets/giswqs/geospatial/resolve/main/naip_train_buildings.geojson"
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raster_path = geoai.download_file(raster_url)
raster_path = geoai.download_file(raster_url)
naip_train.tif: 0%| | 0.00/12.1M [00:00<?, ?B/s]
naip_train.tif: 94%|█████████▍| 11.4M/12.1M [00:00<00:00, 119MB/s]
naip_train.tif: 100%|██████████| 12.1M/12.1M [00:00<00:00, 119MB/s]
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vector_path = geoai.download_file(vector_url)
vector_path = geoai.download_file(vector_url)
naip_train_buildings.geojson: 0%| | 0.00/456k [00:00<?, ?B/s]
naip_train_buildings.geojson: 100%|██████████| 456k/456k [00:00<00:00, 12.9MB/s]
Initialize building footprint extraction pretrained model¶
The pretained model is adapted from the Esri building footprint extraction model for the USA. Credits to Esri for the model.
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extractor = geoai.BuildingFootprintExtractor()
extractor = geoai.BuildingFootprintExtractor()
Model path not specified, downloading from Hugging Face...
Model downloaded to: /home/runner/.cache/huggingface/hub/models--giswqs--geoai/snapshots/f9c3c385b3848cff79f130ae823aeeba0fa8457f/building_footprints_usa.pth Model loaded successfully
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mask_path = extractor.save_masks_as_geotiff(
raster_path=raster_path,
output_path="building_masks.tif",
confidence_threshold=0.5,
mask_threshold=0.5,
)
mask_path = extractor.save_masks_as_geotiff(
raster_path=raster_path,
output_path="building_masks.tif",
confidence_threshold=0.5,
mask_threshold=0.5,
)
Processing masks with parameters: - Confidence threshold: 0.5 - Chip size: (512, 512) - Mask threshold: 0.5 Dataset initialized with 3 rows and 7 columns of chips Image dimensions: 2503 x 1126 pixels Chip size: 512 x 512 pixels Overlap: 25.0% (stride_x=384, stride_y=384) CRS: EPSG:26911
Processing raster with 6 batches
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Object masks saved to building_masks.tif
Convert raster to vector
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gdf = extractor.masks_to_vector(
mask_path=mask_path,
output_path="building_masks.geojson",
simplify_tolerance=1.0,
)
gdf = extractor.masks_to_vector(
mask_path=mask_path,
output_path="building_masks.geojson",
simplify_tolerance=1.0,
)
Converting mask to GeoJSON with parameters: - Mask threshold: 0.5 - Min object area: 100 - Max object area: None - Simplify tolerance: 1.0 - NMS IoU threshold: 0.5 - Regularize objects: True - Angle threshold: 15° from 90° - Rectangularity threshold: 70.0% Mask dimensions: (1126, 2503) Mask value range: 0 to 255 Found 632 potential objects
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100%|██████████| 632/632 [00:00<00:00, 813.88it/s]
Created 609 valid polygons
Object count after NMS filtering: 609 Regularizing 609 objects... - Angle threshold: 15° from 90° - Min orthogonality: 30.0% of angles - Min rectangularity: 70.0% of bounding box area
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Regularization completed: - Total objects: 609 - Rectangular objects: 609 (100.0%) - Other regularized objects: 0 (0.0%) - Unmodified objects: 0 (0.0%) Saved 609 objects to building_masks.geojson
Option 2: Extract building footprints as vector¶
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output_path = "naip_buildings.geojson"
gdf = extractor.process_raster(
raster_path,
output_path="buildings.geojson",
batch_size=4,
confidence_threshold=0.5,
overlap=0.25,
nms_iou_threshold=0.5,
min_object_area=100,
max_object_area=None,
mask_threshold=0.5,
simplify_tolerance=1.0,
)
output_path = "naip_buildings.geojson"
gdf = extractor.process_raster(
raster_path,
output_path="buildings.geojson",
batch_size=4,
confidence_threshold=0.5,
overlap=0.25,
nms_iou_threshold=0.5,
min_object_area=100,
max_object_area=None,
mask_threshold=0.5,
simplify_tolerance=1.0,
)
Processing with parameters: - Confidence threshold: 0.5 - Tile overlap: 0.25 - Chip size: (512, 512) - NMS IoU threshold: 0.5 - Mask threshold: 0.5 - Min object area: 100 - Max object area: None - Simplify tolerance: 1.0 - Filter edge objects: True - Edge buffer size: 20 pixels Dataset initialized with 3 rows and 7 columns of chips Image dimensions: 2503 x 1126 pixels Chip size: 512 x 512 pixels Overlap: 25.0% (stride_x=384, stride_y=384) CRS: EPSG:26911
Processing raster with 6 batches
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Objects before filtering: 711 Objects after filtering: 711 Saved 711 objects to buildings.geojson
Regularize building footprints¶
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gdf_regularized = extractor.regularize_buildings(
gdf=gdf,
min_area=100,
angle_threshold=15,
orthogonality_threshold=0.3,
rectangularity_threshold=0.7,
)
gdf_regularized = extractor.regularize_buildings(
gdf=gdf,
min_area=100,
angle_threshold=15,
orthogonality_threshold=0.3,
rectangularity_threshold=0.7,
)
Regularizing 711 objects... - Angle threshold: 15° from 90° - Min orthogonality: 30.0% of angles - Min rectangularity: 70.0% of bounding box area
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Regularization completed: - Total objects: 711 - Rectangular objects: 664 (93.4%) - Other regularized objects: 0 (0.0%) - Unmodified objects: 47 (6.6%)
Visualize building footprints¶
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gdf.head()
gdf.head()
Out[11]:
| geometry | confidence | class | |
|---|---|---|---|
| 991 | POLYGON ((455092.8 5277652.2, 455092.2 5277651... | 0.988162 | 1 |
| 957 | POLYGON ((455041.2 5277874.2, 455041.2 5277866... | 0.987419 | 1 |
| 435 | POLYGON ((455041.2 5277842.4, 455040.6 5277841... | 0.987318 | 1 |
| 469 | POLYGON ((455072.4 5277718.2, 455071.8 5277717... | 0.986584 | 1 |
| 436 | POLYGON ((455003.4 5277738.6, 455002.8 5277738... | 0.986499 | 1 |
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geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles="Satellite"
)
geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles="Satellite"
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles=raster_url
)
geoai.view_vector_interactive(
gdf, column="confidence", layer_name="Building", tiles=raster_url
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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geoai.view_vector_interactive(
gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url
)
geoai.view_vector_interactive(
gdf_regularized, column="confidence", layer_name="Building", tiles=raster_url
)
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Make this Notebook Trusted to load map: File -> Trust Notebook
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extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")
extractor.visualize_results(raster_path, gdf, output_path="naip_buildings.png")
Using confidence values (range: 0.51 - 0.99)
Visualization saved to naip_buildings.png
Sample visualization saved to naip_buildings_sample.png
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extractor.visualize_results(
raster_path, gdf_regularized, output_path="naip_buildings_regularized.png"
)
extractor.visualize_results(
raster_path, gdf_regularized, output_path="naip_buildings_regularized.png"
)
Using confidence values (range: 0.51 - 0.99)
Visualization saved to naip_buildings_regularized.png
Sample visualization saved to naip_buildings_regularized_sample.png
